Unclear autonomy
Define what the agent may decide, suggest, call, change, or escalate.
Course 03 · 7 days
Agentic AI Developer
Design AI agents that use tools deliberately, manage context, coordinate workflows, and return control to people when the work needs judgement.
1 · Learner problem
An agent is more than a prompt with a tool attached.
Builders need to reason about planning, memory, context, permissions, multi-agent handoffs, failure paths, and the human decisions that must remain visible.
Memory without discipline
Choose the right context and memory pattern without filling the workflow with irrelevant history.
Single agent limits
Understand when planning, specialist agents, or human-in-the-loop patterns make sense.
A production agent plan
Build and test a bounded agentic workflow with explicit tools, controls, and deployment notes.
2 · Course outcome
Leave able to design, evaluate, and explain agentic systems.
Agent workflows
Choose triggers, goals, tools, context, planning, and handoffs for a clear system boundary.
APIs and MCP
Give agents useful tools and context while keeping permissions and outputs constrained.
Memory and multi-agent patterns
Compare memory, context engineering, specialist agents, and orchestration choices.
Production agent engineering
Review evaluation, observability, safety, deployment, and human-in-the-loop controls.
For developers building tool-using AI systems.
- AI agent developers and GenAI engineers
- Software developers building workflow automation
- Technical leads evaluating agentic architectures
- Builders with a process that needs bounded autonomy
Bring a workflow, not a wish for magic.
Programming and LLM application familiarity are useful. The course treats permissions, failure handling, and human ownership as core engineering work.
4 · Curriculum
Eight modules from LLM apps to production agents.
- From LLM Apps to AI AgentsUnderstand the agent boundary and when an agent is the right level of complexity.
- Tool Calling, APIs and MCPConnect agents to tools, APIs, and Model Context Protocol with explicit constraints.
- RAG, Memory and Context EngineeringManage trusted information, memory, context windows, and retrieval patterns.
- Agent Workflows and Multi-Agent SystemsDesign coordination, specialist roles, handoffs, and orchestration.
- Planning, Reasoning and Human-in-the-Loop AgentsMake planning, approval, uncertainty, and escalation visible.
- Production Agent EngineeringReview evaluation, testing, observability, security, and operating controls.
- Deployment + Industry CapstoneDemonstrate an agentic workflow and document its next production step.
5 · Live project
Build an industry-oriented agent capstone.
Choose a support, research, knowledge, operations, or engineering workflow where tool use and human review can be demonstrated.
Agent boundary and tools
Define the trigger, goal, tools, permissions, context, and non-goals.
Failure and approval paths
Exercise uncertainty, tool errors, approvals, and escalation before the final demo.
Deployment walkthrough
Show a bounded run and explain how it would be evaluated and operated.
6 · Trainer credentials
View trainer profileAgentic AI taught with boundaries attached.
MHGrad’s practice includes agents, MCP, RAG, software delivery, operations automation, HR knowledge assistants, and customer-support workflows. Verified trainer credentials and references are available during enrolment conversations.
Seven-day live intensive
- Daily guided instruction and build time
- Agent workflow and failure-path reviews
- Industry capstone demonstration
- Exact dates, timezone, and delivery format confirmed before enrolment
8–10 · Fees and enrolment
Current fee shared on consultation.
Ask about the next intensive, availability, payment steps, or private team delivery. Refund terms are published before payment.
Enquire for current fee
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FAQ and refund terms
Before you enrol.
Is this only for developers?
The course is developer-focused and benefits from programming and LLM application experience. Team versions can include product or operations roles.
What makes an agent production-minded?
Clear boundaries, tool permissions, evaluation, observability, failure handling, and a human owner are part of the design.
Can the capstone use our workflow?
Discuss data access and confidentiality before enrolment. A non-confidential guided workflow is always available.
What is the refund policy?
Eligibility, cut-off dates, transfers, and cancellations are stated in the refund policy and enrolment confirmation.
Build an agent that knows its boundaries.
Ask for dates, fee, prerequisites, or a team-training conversation.